Use Color to Highlight Findings
This lesson is called Use Color to Highlight Findings, part of the Going Deeper with R course. This lesson is called Use Color to Highlight Findings, part of the Going Deeper with R course.
Transcript
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# Load Packages -----------------------------------------------------------
library(tidyverse)
library(fs)
# Create Directory --------------------------------------------------------
dir_create("data")
# Download Data -----------------------------------------------------------
# download.file("https://github.com/rfortherestofus/going-deeper-v2/raw/main/data/third_grade_math_proficiency.rds",
# mode = "wb",
# destfile = "data/third_grade_math_proficiency.rds")
# Import Data -------------------------------------------------------------
third_grade_math_proficiency <-
read_rds("data/third_grade_math_proficiency.rds") |>
select(academic_year, school, school_id, district, proficiency_level, number_of_students) |>
mutate(is_proficient = case_when(
proficiency_level >= 3 ~ TRUE,
.default = FALSE
)) |>
group_by(academic_year, school, district, school_id, is_proficient) |>
summarize(number_of_students = sum(number_of_students, na.rm = TRUE)) |>
ungroup() |>
group_by(academic_year, school, district, school_id) |>
mutate(percent_proficient = number_of_students / sum(number_of_students, na.rm = TRUE)) |>
ungroup() |>
filter(is_proficient == TRUE) |>
select(academic_year, school, district, percent_proficient) |>
rename(year = academic_year) |>
mutate(percent_proficient = case_when(
is.nan(percent_proficient) ~ NA,
.default = percent_proficient
))
# Plot --------------------------------------------------------------------
top_growth_school <-
third_grade_math_proficiency |>
filter(district == "Portland SD 1J") |>
group_by(school) |>
mutate(growth_from_previous_year = percent_proficient - lag(percent_proficient)) |>
ungroup() |>
drop_na(growth_from_previous_year) |>
slice_max(order_by = growth_from_previous_year,
n = 1) |>
pull(school)
third_grade_math_proficiency |>
filter(district == "Portland SD 1J") |>
mutate(highlight_school = case_when(
school == top_growth_school ~ "Y",
.default = "N"
)) |>
mutate(school = fct_relevel(school, top_growth_school, after = Inf)) |>
ggplot(aes(x = year,
y = percent_proficient,
group = school,
color = highlight_school)) +
geom_line() +
scale_color_manual(values = c(
"N" = "grey80",
"Y" = "orange"
))
Your Turn
Highlight the district in your line chart that had the largest increase in its Hispanic/Latino population between 2021-2022 and 2022-2023.
Learn More
The Datawrapper blog has an amazing blog post by Lisa Charlotte Muth on using color effectively in data viz.
The issue that I ran into where the lines weren't visible because there were too many is called overplotting. Claus Wilke discusses overplotting in Chapter 18 of his book Fundamentals of Data Visualization (the chapter is about overplotting with points, but the concepts are the same).
Have any questions? Put them below and we will help you out!
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Matt Newman • May 6, 2024
Really interesting set of lessons here! But I kept making dumb mistakes in my code by confusing "top_growth_hispanic" with "highlight_district." Could you walk through why both steps were needed to make these customizations work? Why create the "highlight_district" variable versus just plotting with the "top_growth_hispanic" value? Thank you!
Patrick Cullen • May 6, 2025
Is the order_by and n = 1 needed here?
slice_max(order_by = growth_from_previous_year, n = 1)
This seems to have the same result:
slice_max(growth_from_previous_year)
Am I correct that this is indexing and ranking based on the max value? So if I wanted the second highest, then I could use order_by and n = 2?
Gracielle Higino Coach • May 6, 2025
Hi Patrick! David always tries to be very explicit on code writing, so he often writes the function arguments even when suppressing them would produce the same results. In this case, however, it's important to be explicit about the
order_by
argument for reproducibility reasons. It happens that in this particular case, defining the order doesn't make a difference, but it could, if you happened to have another variable in the dataset "weighting in" the ranking.Now about the
n
argument, this one is to define the number of rows you want in the slice.n = 1
gets you one row,n = 2
gets you 2 rows. You can also use proportions instead of numbers, using theprop
argument (e.g.,prop = 0.5
).